Why Open Source is not the answer to everything

Today we more and more hear about and tend to use Open Source software. In many cases this might be a viable and good choice. Apparently, mathematical optimization (aka math programming, aka decision optimization) usually is not one of those cases.

Here is a real client story. The client is a major car manufacturer in Germany. Some months ago, they asked one of their common project implementation suppliers to create a new optimization application for one of their business problems. This supplier then implemented an optimization approach that decomposes the original problem into thousands of optimization problems. They then tried an Open Source solver for this problem and this one seemed to do ok on the given problems, at least at first sight.

However, after doing some more testing, it turned out that for real problem sizes this approach had a severe problem: solving all the optimization models on a given hardware would have taken 65 — days! Yes, indeed, more than 2 months. And this was obviously unacceptable for the business users.

The client then reached out to IBM and asked us whether they could try their models with CPLEX. And we gave them a CPLEX evaluation license and then they started testing with CPLEX. And then the first reaction was (literally): “We now tested the first models but this was so fast that we believe something went wrong”.

But actually it was the contrary: nothing went wrong, CPLEX was able to solve the given models to the optimum (on the same hardware, of course) in very short time. The full set of models took less than 2 hours with CPLEX instead of 65 days with the Open Source solver before!

And guess what: line of business was so happy that they purchased CPLEX licenses right away 🙂